To analyze inherent and diverse patterns within line-based public transport daily delay occurrences, we introduce a data-driven exploratory analysis focused on the spatial-temporal distribution of these delays. Our approach relies on the utilization of the image pattern recognition technique and k-means clustering algorithm. We extract daily punctuality information from the automatic vehicle location data for a singular public transport route. This information is then translated into a visual representation through aggregated daily delay distribution profile images, offering insights into the spatial and temporal distribution of delays. The delay distribution finds expression in the arrangement of pixels within these profile images. The essence of these images is further distilled through image pattern recognition using the neural network architecture of ResNet50. Employing the k-means algorithm, we cluster these images based on their similarity, revealing five distinct daily delay patterns. The analysis of these patterns offers insight into their unique characteristics, yielding noteworthy outcomes. These findings hold the potential to provide public transport operators with an enriched comprehension of the dynamics of delays occurring on a specific line.

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On Wednesday, May 29th, MRDH through the XCARCITY Programme organized a round table
on mobility hubs in Rotterdam. About 20 people from the XCARCITY consortium, including 4
researchers and representatives from 10 partner organizations, gathered to discuss the
challenges and opportunities surrounding mobility hubs.
The event was structured into three themes, each addressing critical aspects of mobility hubs.@en